Measure of Information Content of Remotely Sensed Images Accounting for Spatial Correlation

doi:10.11947/j.AGCS.2015.20140417

Abstract

Abstract: A measure is proposed based on the information theory and geostatistics to evaluate information content in remotely sensed images. The method is based on the additive noise model and maximum mutual information.These factors affecting the information content have been taken into account, such as noise, spatial correlation and so on. It is suitable for measuring the information content in optical images that have robust spatial correlation with different land cover types. An experiment was performed on a Landsat TM image with three different kinds of land cover types (city, farmland and mountain). The result shows that city has the most information content. It also proves that there is a log positive correlation between information content and the variance of the images.

Key words: information entropy, information content, variogram, spatial correlation, additive noise, remotely sensed imagery

CLC Number:

P227

ZHANG Ying, ZHANG Jingxiong. Measure of Information Content of Remotely Sensed Images Accounting for Spatial Correlation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(10): 1117-1124.

References

[1] LIN Zongjian, ZHANG Yonghong. Measurement of Information and Uncertainty of Remote Sensing and GIS Data[J]. Geomatics and Information Science of Wuhan University, 2006, 31(7): 569-572. (林宗坚, 张永红. 遥感与地理信息系统数据的信息量及不确定性[J]. 武汉大学学报: 信息科学版, 2006, 31(7): 569-572.)
[2] SHANNON C E. A Mathematical Theory of Communication[J]. Bell System Technical Journal, 1948, 27(3): 379-423.
[3] WANG Zhanhong. A Research on the Metric Model for Remote Sensing Entropy and Quality[D]. Wuhan: Wuhan University, 2004. (王占宏. 遥感影像信息量及质量度量模型的研究[D]. 武汉: 武汉大学, 2004.)
[4] NARAYANAN R M, PONNAPPAN S K, REICHENBACH S E. Effects of Uncorrelated and Correlated Noise on Image Information Content[J]. Geoscience and Remote Sensing Symposium, 2001, 4(7): 1898-1900.
[5] CHEN Yan. Information-Theoretic Research on Information Contents in Remotely Sensed Images[D]. Wuhan; Wuhan University, 2010. (陈艳. 基于信息理论的遥感图像信息度量[D]. 武汉: 武汉大学, 2010.)
[6] LIU Yanfang, LAN Zeying, LIU Yang, et al. Multi-scale Evaluation Method for Uncertainty of Remote Sensing Classification Based on Hybrid Entropy Model[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(1): 82-87. (刘艳芳, 兰泽英, 刘洋, 等. 基于混合熵模型的遥感分类不确定性的多尺度评价方法研究[J]. 测绘学报, 2009, 38(1): 82-87.)
[7] HAN Peng, GONG Jianya, LI Zhilin. A New Approach for Choice of Optimal Spatial Scale in Image Classification Based on Entropy[J]. Geomatics and Information Science of Wuhan University, 2008, 33(7): 676-679. (韩鹏, 龚健雅, 李志林. 基于信息熵的遥感分类最优空间尺度选择方法[J]. 武汉大学学报: 信息科学版, 2008, 33(7): 676-679.)
[8] XU Han, YAN Qin, XU Banlin, et al. The Best Band Selection and Quality Assessment of Multisource Remote Sensing Image Fusion[J]. Science of Surveying and Mapping, 2007, 32(3): 72-74. (许菡, 燕琴, 徐泮林, 等. 多源遥感影像融合最佳波段选择及质量评价研究[J]. 测绘科学, 2007, 32(3): 72-74.)
[9] BLACKNELL D, OLIVER C J. Information Content of Coherent Images[J]. Journal of Physics D: Applied Physics, 1993, 26(9): 1364-1370.
[10] NARAYANAN R M, DESETTY M K, REICHENBACH S E. Effect of Spatial Resolution on Information Content Characterization in Remote Sensing Imagery Based on Classification Accuracy[J]. International Journal of Remote Sensing, 2002, 23(3): 537-553.
[11] AIAZZI B, BARONTI S, SANTURRI L, et al. Information-Theoretic Assessment of Multi-dimensional Signals[J]. Signal Processing, 2005, 85(5): 903-916.
[12] RAZLIGHI Q R, KEHTARNAVAZ N, NOSRATINIA A. Computation of Image Spatial Entropy Using Quadrilateral Markov Random Field[J].IEEE Transactions on Image Processing, 2009, 18(12): 2629-2639.
[13] WOODCOCK C E, STRAHLER A H, JUPP D L B. The Use of Variograms in Remote Sensing: I. Scene Models and Simulated Images[J]. Remote Sensing of Environment, 1988, 25(3): 323-348.
[14] WOODCOCK C E, STRAHLER A H, JUPP D L B. The Use of Variograms in Remote Sensing: II. Real Digital Images[J]. Remote Sensing of Environment, 1988, 25(3): 349-379.
[15] CURRAN P J. The Semivariogram in Remote Sensing—An Introduction[J]. Remote Sensing of Environment, 1988, 24(3): 493-507.
[16] COVER T M, THOMAS J A. Elements of Information Theory[M]. 2nd ed.New York: John Wiley & Sons Inc., 2006.
[17] FU Zuyun. Information Theory—Basic Theory and Application[M]. Beijing: Publishing House of Electronics Industry, 2001. (傅祖芸. 信息论——基础理论与应用[M]. 北京: 电子工业出版社, 2001.)
[18] ZHANG Renlin. Penetration of Information Theory to Science of Surveying and Mapping[J]. Science of Surveying and Mapping, 1994(2): 28-31. (张仁霖. 信息论向测绘科学的渗透[J]. 测绘科学, 1994(2): 28-31.)
[19] CHENG Jicheng, GUO Huadong, SHI Wenzhong, et al. The Undetermined Questions of Remote Sensing Data[M]. Beijing: Science Press, 2004. (承继成, 郭华东, 史文中, 等. 遥感数据的不确定性问题[M]. 北京: 科学出版社, 2004.)
[20] TAO Chunkan, TAO Chunkuang. Optical Information Theory[M]. Beijing: Science Press, 2004. (陶纯堪, 陶纯匡. 光学信息论[M]. 北京: 科学出版社, 2004.)
[21] CORNER B R, NARAYANAN R M, REICHENBACH S E. Noise Estimation in Remote Sensing Imagery Using Data Masking[J]. International Journal of Remote Sensing, 2003, 24(4): 689-702.
[22] VANDER MEER F. Remote-sensing Image Analysis and Geostatistics[J]. International Journal of Remote Sensing, 2012, 33(18): 5644-5676.
[23] ASMAT A, ATKINSON P M, FOODY G M. Geostatistically Estimated Image Noise is a Function of Variance in the Underlying Signal[J]. International Journal of Remote Sensing, 2010, 31(4): 1009-1025.
[24] CURRANP J, DUNGAN J L. Estimation of Signal-to-Noise:A New Procedure Applied to AVIRIS Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(5): 620-628.
[25] CRESSIE N. Fitting Variogram Models by Weighted Least Squares[J]. Journal of the International Association for Mathematical Geology, 1985, 17(5): 563-586.
[26] GRINGARTEN E, DEUTSCH C V. Variogram Interpretation and Modeling[J]. Mathematical Geology, 2001, 33(4): 507-534.